Land Use Classification and Determining the Pattern of Changes for 2014-2017, using OLI Sensor’s Data

آب و خاک

Article 5, Volume 34, Issue 1 - Serial Number 69, March 2020, Pages 55-71   

Document Type: Research Article

DOI: 10.22067/jsw.v34i2.74878

Authors

S.B. Hosseini¹; A. Saremi^{* 2}; M.H. Noury Gheydari³; Hossein Sedghi⁴; A.R. FiroozFar⁵

¹Science and Research branch, Islamic Azad University, Tehran, Iran.

²Science and Research Branch, Islamic Azad University,Tehran,Iran

³Islamic Azad university, Zanjan Branch, Iran

⁴Tehran Science and Research branch, Islamic Azad University, Tehran, Iran

⁵Zanjan University

Abstract

Introduction: Land use is an aggressive process applying to human activities and different uses accomplished over land. It can be argued that human actions can lead to significant changes in current state of earth’s surface. Changes in surface cover (land cover change) may in turn lead to alternations in balance of energy, water, and geochemical fluctuations at local, regional or global levels. Thus, studies on different land uses changes seem necessary in general environmental evaluation. LULC change detection include implementing multi-temporal Remote Sensing (RS) knowledge to analyze the historical LULC data (maps) and therefore helps in determining the trend of changes associated with LULC properties.
Materials and Methods: Image processing and performing supervised image classification helps to extract information from imageries. In this study, ENVI 5.3 software was used for processing two selected imageries in this project (2014 and 2017). Five LULC classes were established as forest, bare land, vegetation, mountain and water body. For each LULC class, 500 samples were collected at least and used for the supervised classification of images in ENVI. About half of these samples, which were used as “training samples” were collected from the study area through Land Surveying Geographical Positioning System GPS (ground truth data) and Google Earth images. The first step in pre-processing of LANDSAT 8 OLI data in this study referred to the collection of training samples for each class and validating the geometric accuracy of Landsat images, while the next step belonged to the conversion of DNs into At-Satellite radiance using algorithms such as FLAASH. Two dated Landsat images were compared via the supervised classification technique. In this classification technique, two or more images with different dates are independently classified. Maximum Likelihood Classification (MLC) algorithm as a supervised classification method was carried out using training areas and test data for accuracy assessment in ENVI 5.3 and accuracy assessment was done for both images using ENVI v5.3.
Results and Discussion: In order to recognize the past land use pattern of Tarom, researchers first focused on imagery of Landsat 8 ETM+ for the year 2014. Summary of supervised classification accuracy for the 2 different time frames (2014 and 2017) found from accuracy assessment showed that the highest accuracy was found for 2014 supervised classification (92.16% accuracy). Kappa value is also used to check accuracy in classification and having a Kappa value (0.81–1.00) denotes almost perfect match between the classified and referenced data. Different LULC classes had been recognized and used as the base map. From the identified LULC classes, Mountain area by 3524 km2 (62.75% of total land area) was the highest category, after which, came bare land areas with 1295 km2 (24.0%) coverage and vegetation area with 194.6 Km2 (3.7%). Forest was the next class with (2.7%) coverage whereas, water body (1.4%) and unknown pixels 8 km2 (0.15%) specified the least amount of coverage, respectively. Based on the 2017 image classification results, the highest category belonged to mountain area (3532 Km2, sharing 67.7% of total area). The remaining land uses were bare land (23.21%), forest (2.73%), vegetation (4.3%), and water body (1.75%). The unknown and uncategorized pixels were identifiable in this stage that shared 0.31% of the total area. The relative changes in land use and land cover from 2014 and 2017 images showed some irregular patterns in the study area. Land-use change from this period showed positive changes in most of the categories. About 31.4 Km2 of vegetation area had increased in 2014–2017 period which showed a positive change of (+16.14%). While a negative decrease (83 Km2, -6.4%) in bare land category. The results showed that the extraction of adequate samples from different classes of land cover/land use would increase the possibility of correct distinction of image pixels received from the satellite and accurate extraction of LULC classes. Thus, obtaining accurate results from the classification of images via the maximum likelihood method is depending on adequate and appropriate training samples. The trend of land-use changes found in this study, especially percentage increase in forest land and a decrease in bare lands will be helpful for policymakers to make appropriate decisions.
Conclusion: Land cover is the physical material at the earth’s surface and an essential variable which links the physical environment by human activities, and land use is the description of how the land has been utilized for the socio-economic activities purposes. Population growth increases the demand for food, water, and energy, which causes a prompt change in land cover and pattern of land-use. The mentioned process depends on the social and economic development of the nation. In order to have appropriate and unrestrictive management of natural resources (water and soil), it is necessary to have complete information about the pattern of land use and its alteration pattern over time. Thus, it can be concluded that remote sensing is a proper technique to investigate the land-use changes using satellite imagery. Spatiotemporal analyses of LULC help us to manage the environmental changes, which are an appropriate tool for decision-makers on water resources’ to enhance their decisions. In the presented study, LULC map for Tarom basin, Iran, acquired from OLI sensor data sets (Landsat-8) by applying a pixel-based classification method (MLC) with the aid of remote sensing technology. The results that are presented in this study proved the usefulness, effectiveness and also convenience of the MLC technique for generating land-use maps by using a free archive of Landsat data and processing the digital images through the ENVI software. Accuracy assessment using overall accuracy and kappa coefficient for 2014 and 2017, shows the performance of the used algorithm. What matters most in this regard is the accuracy, speed, and quality of land-use maps. In the present study, it was shown that due to high speed and accuracy in generating land-use maps of Tarom, MLC method, would act as the best classification method in this area. However, it is suggested to classify the data by using other methods and compare the results with image outputs provided by Landsat 8 satellite.

Keywords

ENVI; Kappa Coefficient; LANDSAT 8; Land Use Classification; Tarom Basin

References

1- Afify H.A. 2011. Evaluation of change detection techniques for monitoring landcover changes: a case study in new Burg El-Arab area. Alexandria Engineering Journal 50: 187–195.

2- Agarwal C., Green G.M., Grove J.M., Evans T.P., and Schweik C.M. 2001. A Review andAssessment of Land-Use Change Models Dynamics of Space, Time, and HumanChoice. CIPEC Collaborative Report Series No. 1, Center for the Study ofInstitutions Population, and Environmental Change Indiana University.

3- Akbari E., Zangane Asadi M.A., and Taghavi E. 2016. Change detectionn land use and land cover regional neyshabour using Different methods of statistical training theory. Geographical Planning of Space 6(20): 35-50. (In Persian with English abstract)

4- Akyürek D., Koç O., Akbaba E.M., and Sunar F. 2018. Land use/Land Cover Change Detection Using Multi-Temporal Satellite Dataset: A case Study in Istanbul New Airport. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W4, Geo Information for Disaster Management, 18–21 March, Istanbul, Turkey.

5- Al-Bilbilsi H. 2019. Spatial Monitoring of Urban Expansion Using Satellite Remote Sensing Images: A Case Study of Amman City, Jordan. Sustainability 11(8): 1-14. doi:10.3390/su11082260.

6- Anderson G.P., Felde G.W., Hoke M.L., Ratkowski A.J., et al. 2002. MODTRAN4-based atmospheric correction algorithm: FLAASH (fast line-of-sight atmospheric analysis of spectral hypercubes). In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultra Spectral Imagery VIII (Proceedings of SPIE); She SS, Lewis P., Eds.; Society of Photo Optics: Orlando FL, USA: 65–71.

7- Asghari Saraskanroud S., Aghayary L., and Pirouzi E. 2018. Study of land use change and its effect on erosion in Nir city using GIS and RS (Case study: Nir county). Journal of RS and GIS for Natural Resources 8(4): 49-62. (In Persian with English abstract)

8- Aspinall R. 2004. Modelling land use change with generalized linear models– A multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana. Journal of Environmental Management 72: 91–103.

9- Avci Z.D.U., Karaman M., Ozelkan E., and Papila I. 2011. A Comparison of Pixel-Based and Object-Based Classification Methods, a Case Study: Istanbul, Turkey. 34th International Symposium on Remote Sensing of Environment, Sydney, Australia.

10- Azartaj E., Rasoolzade A., and Esmali oori A. 2014. Investigation of the Impact of Land Use Changes on Soil Erosion and Surface Runoff Using Precipitation Simulation (Case Study: Band Almas, Ardabil Province). 1st National Conference on Sustainable Management of Soil and Environmental Resources. 10-11 Sep. 2003. Shahid Bahonar University, Kerman, Iran.

11- Azartaj E., Rasoulzadeh A., and Asghari A. 2018. Investigation of land use change effect on runoff and soil erosion using rainfall simulation in Heiran area, Ardabil. Watershed Engineering and Management 10(1): 1-13. (In Persian with English abstract)

12- Baatz M., and Schäpe A. 1999. Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation, XI. Beitrage zum AGIT Symposium, Salzburg, Germany.

13- Benedictsson J.A., Swain P.H., and Ersoy O.K. 1990. Neural Network Approaches versus Statistical Methods in Classification of Multisource Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing 28(4): 540–551.

14- Brandt J.S., Haynes M.A., Kuemmerle T., Waller D.M., and Radeloff V.C. 2013. Regime shift on the roof of the world: alpine meadows converting to shrublands in the southern Himalayas. Biological Conseration 158: 116–127.

15- Carlson T.N., and Azofeifa S.G.A. 1999. Satellite Remote Sensing of land Use changes in and around San Jose´, Costa Rica. Remote Sensing of Environment 70: 247–256.

16- Chaikaew P. 2018. Land Use Change Monitoring and Modelling using GIS and Remote Sensing Data for Watershed Scale in Thailand. IntechOpen, 165-181. DOI: 10.5772/intechopen.79167.

17- Chen J., Zhu X., Vogelmann J.E., Gao F., and Jin S. 2011. A simple and effective method for filling gaps in Landsat ETM+ slc-off images. Remote Sensing Environment 115(4): 1053–1064.

18- Dewan A.M., and Yamaguchi Y. 2009. Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Applied Geography 29: 390–401.

19- Dutta D., Kundu A., Patel N.R., Saha S.K., and Siddiqui A.R. 2015. Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). The Egyptian Journal of Remote Sensing and Space Sciences 18: 53–63.

20- Economic Development and Rural Employment Program in Zanjan Province. 2018. Plan and Budget Organization, Presidency Islamic republic of Iran. (In Persian)

21- Elhag M., and Boteva S. 2016. Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data. 5–9 Sep. 2016. IOP Conference Series Earth and Environmental Science 44(4): 042032.

22- Fallah Sourki M., Kavian A., and Omidvar E. 2016. Prioritizitzation of Haraz sub-watersheds in order to Soil and Water Conservation Practices Based on Morphometric and Land Use Characteristics. Journal of Science and Technology of Agriculture and Natural Resources 20(77): 85-99. (In Persian with English abstract)

23- Fan F., Weng Q., and Wang Y. 2007. Land use land cover change in Guangzhou, China, from 1998 to 2003, based on Landsat TM/ETM+ imagery. Sensors 7: 1323-1342.

24- Feizizadeh B. 2017. Modeling the Trends of the Land Use/Cover Change and Its Impacts on the Erosion System of the Allavian Dam Based on the Remote Sensing and GIS Techniques. Hydrogeomorphology 3(11): 21-38. (In Persian with English abstract)

25- Geist H.J. 2005. The land-use and cover change (lulc) project. Land use, land cover and soil sciences, I. Retrieved from http://www.eolss.net/sample-chapters/c19/E1-05.pdf.

26- Habib A.F., Kersting A.P., Shaker A., and Yan W.Y. 2011. Geometric Calibration and Radiometric Correction of LiDAR Data and Their Impact on the Quality of Derived Products. Sensores 11(9): 9069-9097.

27- Hasheminasab S.N., and Jafai R. 2018. Evaluation of Land Use Changes order to Desertification Monitoring Using Remote Sensing Techniques. Journal of Spatial Analysis Environmental Hazards 5(3): 67-82. (In Persian with English abstract)

28- Hosseini Y., Ramezani Moghadam J., and Abdolali zade Z. 2019. Evaluating the Impact of Land Use Changes on Flooding and Flood Runoff in Amuqin Drainage Basin. Natural Environmental Hazards, (in press) DOI: 10.22111/JNEH.2019.27508.1464

29- Hu Y., Dong Y., and Batunacun. 2018. An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support. ISPRS Journal of Photogrammetry and Remote Sensing 146: 347–359.

30- Iqbal M.F., and Khan I.A. 2014. Spatiotemporal land use land cover change analysis and erosion risk mapping of Azad Jammu and Kashmir, Pakistan. The Egyptian Journal of Remote Sensing and Space Sciences 17: 209–229.

31- Islam K., Jashimuddin M., Nath B., and Nath T.K. 2016. Quantitative Assessment of land cover change using landsat time series data: case of Chunati Wildlife Sanctuary (CWS), Bangladesh. International Journal of Environment Geoinformatics 3: 45–55.

32- Islam K., Jashimuddin M., Nath B. and Nath T.K. 2018. Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. The Egyptian Journal of Remote Sensing and Space Sciences 21: 37-47.

33- Jensen J.R. 1996. A remote sensing perspective. In Introductory Digital Image Processing; Prentice Hall: Englewood Cliffs, NJ, USA.

34- Jensen J. 2005. Introductory digital image processing: A remote sensing perspective (3rd Ed.). Upper Saddle River, NJ: Prentice Hall. 526 pp.

35- Kantakumar L.N., and Neelamsetti P. 2015. Multi-temporal land use classification using hybrid approach. The Egyptian Journal of Remote Sensing and Space Sciences 18: 289–295.

36- Karimi Firozjaei M., Kiyavarz M., and Kalantari, M. 2017. Monitoring and prediction of land use changes and physical expansion of Babol city during 1985-2040 using multi-temporal Landsat imagery. Physical Development Planning 3(7): 32-52. (In Persian with English abstract)

37- Kazemi M., Nohegar A., and Mirdadi M. 2017. Comparison of different classification algorithms in Landsat OLI imagery to produce land use maps (Case study: Beheshte Gomshode region). Journal of Natural Ecosystems of Iran 8(1): 79-97. (In Persian)

38- Kyani V., Alizadeh Shaabani A., and Nazari Samani A. 2014. Assessing the Classification accuracy of LISS-III Sensor Image of IRS-P6 Satellite using Google Earth'sDatabase to provide land coverage/ Land use maps (Case study: Taleghan Watershed). Geographical Data 23(90): 51-59. (In Persian with English abstract)

39- Kumar R., Nandy S., and Agarwal R. 2014. Kushwaha, S.P.S. Forest cover dynamics analysis and prediction modeling using logistic regression model. Ecological Indicators 45: 444–455.

40- Lal A.M., and Margret Anouncia S. 2015. Semi-supervised change detection approach combining sparse fusion and constrained k means for multi-temporal remote sensing images The Egyptian Journal of Remote Sensing and Space Sciences 18: 279–288.

41- Lambin E.F. 1997. Modelling and monitoring land cover change processes in tropical regions. Progress in Physical Geography 21:375–393.

42- Lambin E.F., Turner B.L., Geist H.J., Agbola S.B., Angelsen A., Folke C., Bruce J.W., Coomes O.T., et al. 2001. The causes of land-use and landcover change: moving beyond the myths. Global Environment Change 11: 261–269.

43- Landis J.R., and Koch G.G. 1977. The measurement of observer agreement for categorical data. Biometrics 33(1): 159–174.

44- Lopez E., Bocco G., Mendoza M., and Duhau E. 2001. Predicting land cover and land use change in the urban fringe. Landscape Urban Plann 55(4): 271–285.

45- Lopez-Serrano P.M., Corral-Rivas J.J., Diaz-Varela R.A., Álvarez-Gonzalez J.G. and Lopez-Sanchez, C.A. 2016. Evaluation of radiometric and atmospheric correction algorithms for aboveground forest biomass estimation using landsat 5 TM data. Remote Sensing 8(5): 1–19.

46- Mahmoodi M.A., and Aminkhah S. 2018. Providing Land Use and Land Cover Maps Using Remote Sensing Data and Artificial Neural Network. Iranian Journal of Soil and Water Research 49(5): 1171-1180. (In Persian with English abstract)

47- Malarvizhi K., Kumar S.V., and Porchelvan P. 2016. Use of High Resolution Google Earth Satellite Imagery in Landuse Map Preparation for Urban Related Applications. Procedia Technology 24: 1835–1842.

48- Mamun A.Al, Mahmood A., and Rahman M. 2013. Identification and Monitoring the Change of Land Use Pattern Using Remote Sensing and GIS: A Case Study of Dhaka City. Journal of Mechanical and Civil Engineering 6(2): 20–28.

49- Manakos I., Manevski K., Kalaitzidis Ch., and Edler D. 2011. Comparison between FLAASH & ATCOR atmospheric correction modules on the basis of WorldView-2 imagery and in situ spectroradiometric measurements. EARSeL 7th SIG-Imaging Spectroscopy Workshop, Edinburgh 11-13 April.

50- Marangoz A.M., Sekertekin A., and Akcin H. 2017. Analysis of Land Use Land Cover Classification Results Derived from Sentinel-2 Image. Conference: 17th International Multidisciplinary Scientific Geo-Conference at: Albena, Varna, Bulgaria.

51- Mohajne M., Essahlaoui A., Oudija, F., et al. 2018. Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 5(12): 2-16.

52- Mohammady M., Moradi H.R., Zeinivand H., and Temme A.J.A.M. 2015. A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran. International Journal of Environmental Science and Technology 12(5): 1515-1526.

53- Mombeni M., and Asgari H. 2018. Monitoring, assessment and prediction of spatial changes of Land Use /Cover using Markov Chain Model (Case study: Shushtar- Khuzestan). Geograohical Data 27(105): 35-47.

54- Mondal M.S., Sharma N., Kappas M., and Garg P.K. 2012. Modeling of spatio-temporal dynamics of LULC – a review and assessment. Journal of Geomatics 6(2): 93–103.

55- Mondal M.S., Sharma N., Garg P.K., and Kappas M. 2016. Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. The Egyptian Journal of Remote Sensing and Space Sciences 19(2): 259–272.

56- Muttitanon W., and Tiıpathi N.K. 2005. Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. International Journal of Remote Sensing 26(11): 2311-2323.

57- Pullanikkatil D., Palamuleni L., and Ruhiiga T. 2016. Assessment of land use change in Likangala River catchment, Malawi: A remote sensing and DPSIR approach. Applied Geography 71: 9–23.

58- Rafii S., Alavipanah B., malekmohammadi B., ramazani Mehrian M., and Nasiri H. 2012. Producing land cover maps using remote sensing and decision tree algorithm (Case study: Bakhtegan national park and wildlife refuge). Geography and Environmental Planning 23(3): 111-132. (In Persian with English abstract)

59- Rawat J.S., and Kumar M. 2015. Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Sciences 18: 77–84.

60- Reis S. 2008. Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey. Sensors 8:6188-6202.

61- Riahi bakhtiary H.R., Darvish sefat A.A., and Zobairy M. 2000. Determining the most suitable method for preparing natural resource land use maps in scale of 1: 250,000 using satellite data in the Arzhan plain area. Geomatics, Tehran, Iran.

62- San B.T., and Suzan M.L. 2010. Evaluation of Different Atmospheric Correction Algorithms for EO-1 Hyperion Imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science (XXXVIII)8: 392-397.

63- Shenani Hoveize S.M., and Zareii H. (2017). Investigation of Land Use Changes During the Past Two Last Decades (Case Study: Abolabas Basin). Journal of Watershed Management Research 7(14): 237-244. (In Persian with English abstract)

64- Sophia S.R., and Ndambuki J.M. 2017. Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences 8: 611-622.

65- Song C., Woodcock C.E., Seto K.C., Lenney M.P., and Macomber S.A. 2001. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sensing of Environment 75: 230–244.

66- Sustainable Rural Development Sector Plan in Centern of Tarom, Iran. 2017. Housing Foundation of Islamic Revolution. Pp: 674. (In Persian)

67- Ulbricht K.A., and Heckendorf W.D. 1998. Satellite images for recognition of landscape and land use changes. ISPRS Journal of Photogrammetry & Remote Sensing 53: 235-243.

68- Van Vliet J., Bregt A.K., and Hagen-Zanker A. 2011. Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecological Modelling 222(8): 1367–1375.

69- Vescovi F.D., Park S.J., and Vlek P.L. 2002. Detection of human-induced land coverchanges in a savannah landscape in Ghana: I. Change detection andquantification. In 2nd Workshop of the EARSeL Special Interest Group on Remote Sensing for Developing Countries. Bonn, Germany.

70- Yang X., and Lo C.P. 2002. Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. Internationa Journal of Remote Sensing 23(9): 1775–1798.

71- Zeng Y.N., Wu G.P., Zhan F.B., and Zhang H.H. 2008. Modeling spatial land use pattern using autologistic regression. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVII: 115–118.

72- Zhao G.X., Lin G., and Warner T. 2008. Using ThematicMapper data for change detection and sustainable use of cultivated land: a case study in the Yellow River delta, China. International. Journal of Remote Sensing 25(13): 2509-2522.

73- Zhu Z., Fu Y., Woodcock C.E., Olofsson P., Vogelmann J.E., Holden C., Wang M., Dai S., and Yu Y. 2016. Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014). Remote Sensing of Environment 185: 243–257.

74- Zsuzsanna D., Bartholy J., Pongracz R., and Barcza Z. 2005. Analysis of land-use/land-cover change in the Carpathian region based on remote sensing techniques. Physics and Chemistry of Earth 30(1-3): 109-115.